ML Anthology

Data Dependent Concentration Bounds for Sequential Prediction Algorithms

Tong Zhang
COLT 2005 pp. 173-187
doi:10.1007/11503415_12 /colt/2005/zhang2005colt-data/

Abstract

We investigate the generalization behavior of sequential prediction (online) algorithms, when data are generated from a probability distribution. Using some newly developed probability inequalities, we are able to bound the total generalization performance of a learning algorithm in terms of its observed total loss. Consequences of this analysis will be illustrated with examples.

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Text

Zhang. "Data Dependent Concentration Bounds for Sequential Prediction Algorithms." Annual Conference on Computational Learning Theory, 2005. doi:10.1007/11503415_12

Markdown

[Zhang. "Data Dependent Concentration Bounds for Sequential Prediction Algorithms." Annual Conference on Computational Learning Theory, 2005.](https://mlanthology.org/colt/2005/zhang2005colt-data/) doi:10.1007/11503415_12

BibTeX

@inproceedings{zhang2005colt-data,
  title     = {{Data Dependent Concentration Bounds for Sequential Prediction Algorithms}},
  author    = {Zhang, Tong},
  booktitle = {Annual Conference on Computational Learning Theory},
  year      = {2005},
  pages     = {173-187},
  doi       = {10.1007/11503415_12},
  url       = {https://mlanthology.org/colt/2005/zhang2005colt-data/}
}